Information processing apparatus, learning apparatus, and information processing method

Abstract

An information processing apparatus comprises: a conversion unit configured to convert an input image of a first bit depth into a low-bit-depth image of a second bit depth lower than the first bit depth; an estimation unit configured to estimate a noise component map in the input image from the low-bit-depth image using a neural network (NN) of a third bit depth that is lower than the first bit depth and is not lower than the second bit depth; and a deriving unit configured to derive a noise-reduced image corresponding to the input image based on the input image and the noise component map.

What is claimed is: 
     
         1 . An information processing apparatus comprising:
 a conversion unit configured to convert an input image of a first bit depth into a low-bit-depth image of a second bit depth lower than the first bit depth;   an estimation unit configured to estimate a noise component map in the input image from the low-bit-depth image using a neural network (NN) of a third bit depth that is lower than the first bit depth and is not lower than the second bit depth; and   a deriving unit configured to derive a noise-reduced image corresponding to the input image based on the input image and the noise component map.   
     
     
         2 . The apparatus according to  claim 1 , wherein
 the estimation unit estimates the noise component map by estimating an intermediate noise component map of the third bit depth from the low-bit-depth image using the NN, and performing bit depth conversion for the intermediate noise component map into the first bit depth, and   the deriving unit derives the noise-reduced image by subtracting the noise component map from the input image.   
     
     
         3 . The apparatus according to  claim 2 , wherein
 the NN includes a conversion layer for the bit depth conversion, and   the bit depth conversion includes nonlinear conversion.   
     
     
         4 . The apparatus according to  claim 2 , wherein
 the bit depth conversion of the NN nonlinearly converts the intermediate noise component map clipped by a threshold into the third bit depth.   
     
     
         5 . The apparatus according to  claim 4 , wherein
 the bit depth conversion of the NN performs, for the intermediate noise component map nonlinearly converted into the third bit depth, nonlinear conversion by an inverse function of the nonlinear conversion, thereby performing conversion into an intermediate noise component map having tones of the third bit depth having the same range as a range of the threshold.   
     
     
         6 . The apparatus according to  claim 3 , wherein
 the nonlinear conversion is performed by using a lookup table (LUT) or by an arithmetic operation, and   the arithmetic operation includes an operation by a piecewise linear function.   
     
     
         7 . The apparatus according to  claim 1 , wherein
 the third bit depth is equal to the second bit depth.   
     
     
         8 . The apparatus according to  claim 1 , wherein
 the conversion unit executes processing including nonlinear conversion at the time of converting the input image of the first bit depth into the low-bit-depth image of the second bit depth lower than the first bit depth.   
     
     
         9 . The apparatus according to  claim 8 , wherein
 in the processing including the nonlinear conversion, a value closer to a black level is converted into finer tones.   
     
     
         10 . A learning apparatus for learning an NN of an information processing apparatus defined in  claim 1 , comprising:
 a first obtaining unit configured to obtain a clean image of a first bit depth without noise and a noise component map to be added to the clean image;   a second obtaining unit configured to obtain a noisy image of the first bit depth by adding the noise component map to the clean image;   a second conversion unit configured to convert the noisy image into a low-bit-depth image of a second bit depth;   a second estimation unit configured to estimate, by using the NN, an estimation map as a result of estimating the noise component map from the low-bit-depth image; and   an update unit configured to update a parameter of the NN based on an error between the estimation map and the noise component map.   
     
     
         11 . The apparatus according to  claim 10 , wherein
 the update unit updates the parameter of the NN by backpropagation.   
     
     
         12 . An information processing method for an information processing apparatus, comprising:
 converting an input image of a first bit depth into a low-bit-depth image of a second bit depth lower than the first bit depth;   estimating a noise component map in the input image from the low-bit-depth image using a neural network (NN) of a third bit depth that is lower than the first bit depth and is not lower than the second bit depth; and   deriving a noise-reduced image corresponding to the input image based on the input image and the noise component map.